Systematic review and meta-analysis are often confused and used interchangeably albeit having distinct differences between them. Both are reviews of high quality evidence that form a part of literature review.  What is a Systematic review?  A systemic review seeks to find answer to a defined question by collecting and synthesizing high quality evidence from different studies. It is an exhaustive summary of all scientific evidence substantiated to a particular question. Systematic reviews came to be as a way to improve the quality of literature review. Systematic review applies to all types of research, both qualitative and quantitative. Systematic review evolved in the health care industry and clinical trials, but has since found its application in variety of fields like social sciences, astronomy, etc. What is a Meta-analysis?   Meta-analysis integrates statistically similar studies using statistical principles to achieve an estimate of common po...

Significance of Statistics in Research Being an integral part of any research related to most subjects, Statistics and its importance in research have been recognized since long. Quality research is invariably in most topic cover portions of statistics. Statistics is linked to a wide range of research activities. This is where scholars feel the need for effective  Ph.D. statistics help  for pursuing their research. Apart from subject knowledge, researchers need to have expertise in statistical methods also. For validating the findings, statistical portions are being made mandatory by institutions across the globe. It is by having theoretical knowledge as well as skill in the application of statistics that researchers will be able to gain an extra edge, making  PhD statistics help  a vital factor. “The goal is to turn data into information, and information into insight.” – Carly Fiorina, former CEO, Hewlett-Packard Co. Conducting Statistical Tests ...

The main purpose of this blog is to understand the Testing Group Differences using T-tests, ANOVA, and Non parametric Measures. Choosing the right test for your data analysis is a very difficult task particularly identifying the Different methods from testing group differences is the Biggest challenging task. It is important to have to in-depth Knowledge to understand and calculate T-tests, ANOVA, and Nonparametric, brief interpretation of the output. In order to choose the right statistical test, when analyzing the data from an experiment, we must have a good understanding of some basic statistical terms and concepts: Test for Normality Every data must follow certain distribution. But we have to find the appropriate distribution from goodness of fit test. So, our data is checked through each and every distribution. Hence, goodness of fit test is very tedious. This way of estimation of data is called by parametric tests. Parametric tests always give the reliable estimated...

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